Overview

Dataset statistics

Number of variables12
Number of observations276
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.0 KiB
Average record size in memory104.0 B

Variable types

Text2
Numeric10

Alerts

All Grades is highly overall correlated with Total Students and 3 other fieldsHigh correlation
Total Students is highly overall correlated with All Grades and 3 other fieldsHigh correlation
Primary is highly overall correlated with All Grades and 1 other fieldsHigh correlation
Secondary is highly overall correlated with All Grades and 2 other fieldsHigh correlation
Others-% is highly overall correlated with All Grades and 2 other fieldsHigh correlation
Tests Taken is highly overall correlated with Total Students and 1 other fieldsHigh correlation
% Score 3-5 is highly overall correlated with Total Students and 1 other fieldsHigh correlation
District Name_x has unique valuesUnique
District Code has unique valuesUnique
District Name_y has unique valuesUnique
All Grades has 64 (23.2%) zerosZeros
Primary has 206 (74.6%) zerosZeros
Secondary has 98 (35.5%) zerosZeros
High has 149 (54.0%) zerosZeros
Others-% has 64 (23.2%) zerosZeros
Tests Taken has 59 (21.4%) zerosZeros
% Score 1-2 has 79 (28.6%) zerosZeros
% Score 3-5 has 82 (29.7%) zerosZeros

Reproduction

Analysis started2023-07-02 22:22:45.443794
Analysis finished2023-07-02 22:22:52.741872
Duration7.3 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

District Name_x
Text

UNIQUE 

Distinct276
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
2023-07-02T18:22:52.867653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length61
Median length56
Mean length16.474638
Min length3

Characters and Unicode

Total characters4547
Distinct characters55
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique276 ?
Unique (%)100.0%

Sample

1st rowAbington
2nd rowAcademy Of the Pacific Rim Charter Public (District)
3rd rowActon-Boxborough
4th rowAdvanced Math and Science Academy Charter (District)
5th rowAgawam
ValueCountFrequency (%)
district 34
 
6.2%
regional 28
 
5.1%
charter 27
 
5.0%
vocational 22
 
4.0%
technical 21
 
3.9%
school 16
 
2.9%
academy 10
 
1.8%
public 7
 
1.3%
north 7
 
1.3%
of 6
 
1.1%
Other values (304) 367
67.3%
2023-07-02T18:22:53.131329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 380
 
8.4%
o 350
 
7.7%
a 327
 
7.2%
t 317
 
7.0%
r 306
 
6.7%
i 289
 
6.4%
n 271
 
6.0%
269
 
5.9%
l 261
 
5.7%
c 189
 
4.2%
Other values (45) 1588
34.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3616
79.5%
Uppercase Letter 576
 
12.7%
Space Separator 269
 
5.9%
Open Punctuation 28
 
0.6%
Close Punctuation 28
 
0.6%
Dash Punctuation 27
 
0.6%
Other Punctuation 3
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 380
10.5%
o 350
9.7%
a 327
9.0%
t 317
8.8%
r 306
8.5%
i 289
 
8.0%
n 271
 
7.5%
l 261
 
7.2%
c 189
 
5.2%
h 172
 
4.8%
Other values (15) 754
20.9%
Uppercase Letter
ValueCountFrequency (%)
S 62
 
10.8%
C 57
 
9.9%
R 45
 
7.8%
D 45
 
7.8%
M 37
 
6.4%
A 36
 
6.2%
B 35
 
6.1%
W 33
 
5.7%
T 33
 
5.7%
V 30
 
5.2%
Other values (13) 163
28.3%
Other Punctuation
ValueCountFrequency (%)
: 1
33.3%
' 1
33.3%
. 1
33.3%
Space Separator
ValueCountFrequency (%)
269
100.0%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4192
92.2%
Common 355
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 380
 
9.1%
o 350
 
8.3%
a 327
 
7.8%
t 317
 
7.6%
r 306
 
7.3%
i 289
 
6.9%
n 271
 
6.5%
l 261
 
6.2%
c 189
 
4.5%
h 172
 
4.1%
Other values (38) 1330
31.7%
Common
ValueCountFrequency (%)
269
75.8%
( 28
 
7.9%
) 28
 
7.9%
- 27
 
7.6%
: 1
 
0.3%
' 1
 
0.3%
. 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4547
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 380
 
8.4%
o 350
 
7.7%
a 327
 
7.2%
t 317
 
7.0%
r 306
 
6.7%
i 289
 
6.4%
n 271
 
6.0%
269
 
5.9%
l 261
 
5.7%
c 189
 
4.2%
Other values (45) 1588
34.9%

District Code
Real number (ℝ)

UNIQUE 

Distinct276
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3935615.9
Minimum0
Maximum39020000
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-07-02T18:22:53.224551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile257500
Q11477500
median2845000
Q36227500
95-th percentile8512500
Maximum39020000
Range39020000
Interquartile range (IQR)4750000

Descriptive statistics

Standard deviation4320424.3
Coefficient of variation (CV)1.0977759
Kurtosis33.368319
Mean3935615.9
Median Absolute Deviation (MAD)1850000
Skewness4.7329688
Sum1.08623 × 109
Variance1.8666066 × 1013
MonotonicityNot monotonic
2023-07-02T18:22:53.300387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 1
 
0.4%
2200000 1
 
0.4%
2290000 1
 
0.4%
8600000 1
 
0.4%
2270000 1
 
0.4%
2260000 1
 
0.4%
7400000 1
 
0.4%
8550000 1
 
0.4%
2190000 1
 
0.4%
2170000 1
 
0.4%
Other values (266) 266
96.4%
ValueCountFrequency (%)
0 1
0.4%
10000 1
0.4%
50000 1
0.4%
70000 1
0.4%
90000 1
0.4%
100000 1
0.4%
140000 1
0.4%
160000 1
0.4%
170000 1
0.4%
180000 1
0.4%
ValueCountFrequency (%)
39020000 1
0.4%
35060000 1
0.4%
35030000 1
0.4%
9100000 1
0.4%
8850000 1
0.4%
8790000 1
0.4%
8780000 1
0.4%
8760000 1
0.4%
8730000 1
0.4%
8720000 1
0.4%

All Grades
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct188
Distinct (%)68.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean839.02174
Minimum0
Maximum121772
Zeros64
Zeros (%)23.2%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-07-02T18:22:53.379231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median199
Q3567.5
95-th percentile1526.75
Maximum121772
Range121772
Interquartile range (IQR)562.5

Descriptive statistics

Standard deviation7326.4694
Coefficient of variation (CV)8.7321568
Kurtosis272.86172
Mean839.02174
Median Absolute Deviation (MAD)199
Skewness16.472944
Sum231570
Variance53677154
MonotonicityNot monotonic
2023-07-02T18:22:53.458835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 64
 
23.2%
49 4
 
1.4%
6 3
 
1.1%
119 3
 
1.1%
179 2
 
0.7%
1 2
 
0.7%
28 2
 
0.7%
113 2
 
0.7%
120 2
 
0.7%
13 2
 
0.7%
Other values (178) 190
68.8%
ValueCountFrequency (%)
0 64
23.2%
1 2
 
0.7%
2 2
 
0.7%
5 2
 
0.7%
6 3
 
1.1%
8 1
 
0.4%
13 2
 
0.7%
15 1
 
0.4%
16 1
 
0.4%
18 1
 
0.4%
ValueCountFrequency (%)
121772 1
0.4%
3925 1
0.4%
3129 1
0.4%
2716 1
0.4%
2062 1
0.4%
2007 1
0.4%
1939 1
0.4%
1885 1
0.4%
1873 1
0.4%
1835 1
0.4%

Total Students
Real number (ℝ)

HIGH CORRELATION 

Distinct267
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3912.1884
Minimum0
Maximum557504
Zeros2
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-07-02T18:22:54.422910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile192.75
Q1848.5
median1540
Q32616.25
95-th percentile4585.75
Maximum557504
Range557504
Interquartile range (IQR)1767.75

Descriptive statistics

Standard deviation33478.353
Coefficient of variation (CV)8.5574491
Kurtosis274.83933
Mean3912.1884
Median Absolute Deviation (MAD)848
Skewness16.561189
Sum1079764
Variance1.1208001 × 109
MonotonicityNot monotonic
2023-07-02T18:22:54.496321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1844 2
 
0.7%
951 2
 
0.7%
862 2
 
0.7%
1311 2
 
0.7%
141 2
 
0.7%
692 2
 
0.7%
2517 2
 
0.7%
2034 2
 
0.7%
0 2
 
0.7%
989 1
 
0.4%
Other values (257) 257
93.1%
ValueCountFrequency (%)
0 2
0.7%
22 1
0.4%
23 1
0.4%
27 1
0.4%
36 1
0.4%
39 1
0.4%
87 1
0.4%
130 1
0.4%
141 2
0.7%
153 1
0.4%
ValueCountFrequency (%)
557504 1
0.4%
10247 1
0.4%
9217 1
0.4%
7797 1
0.4%
7370 1
0.4%
7055 1
0.4%
5985 1
0.4%
5794 1
0.4%
5681 1
0.4%
5102 1
0.4%

Primary
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)25.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean238.62319
Minimum0
Maximum35619
Zeros206
Zeros (%)74.6%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-07-02T18:22:54.571838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39.5
95-th percentile645.5
Maximum35619
Range35619
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation2152.5636
Coefficient of variation (CV)9.0207647
Kurtosis268.21827
Mean238.62319
Median Absolute Deviation (MAD)0
Skewness16.267065
Sum65860
Variance4633530.2
MonotonicityNot monotonic
2023-07-02T18:22:54.644034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 206
74.6%
360 1
 
0.4%
585 1
 
0.4%
484 1
 
0.4%
824 1
 
0.4%
471 1
 
0.4%
584 1
 
0.4%
248 1
 
0.4%
178 1
 
0.4%
227 1
 
0.4%
Other values (61) 61
 
22.1%
ValueCountFrequency (%)
0 206
74.6%
6 1
 
0.4%
20 1
 
0.4%
26 1
 
0.4%
44 1
 
0.4%
73 1
 
0.4%
82 1
 
0.4%
92 1
 
0.4%
119 1
 
0.4%
140 1
 
0.4%
ValueCountFrequency (%)
35619 1
0.4%
1734 1
0.4%
1404 1
0.4%
1298 1
0.4%
1110 1
0.4%
1014 1
0.4%
970 1
0.4%
882 1
0.4%
871 1
0.4%
824 1
0.4%

Secondary
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct161
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean569.52536
Minimum0
Maximum81769
Zeros98
Zeros (%)35.5%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-07-02T18:22:54.722022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median142.5
Q3413.25
95-th percentile1016.75
Maximum81769
Range81769
Interquartile range (IQR)413.25

Descriptive statistics

Standard deviation4919.0766
Coefficient of variation (CV)8.637151
Kurtosis272.91152
Mean569.52536
Median Absolute Deviation (MAD)142.5
Skewness16.47499
Sum157189
Variance24197314
MonotonicityNot monotonic
2023-07-02T18:22:54.797644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 98
35.5%
152 4
 
1.4%
49 3
 
1.1%
285 2
 
0.7%
240 2
 
0.7%
92 2
 
0.7%
111 2
 
0.7%
575 2
 
0.7%
257 2
 
0.7%
345 2
 
0.7%
Other values (151) 157
56.9%
ValueCountFrequency (%)
0 98
35.5%
6 1
 
0.4%
13 1
 
0.4%
16 1
 
0.4%
26 1
 
0.4%
31 1
 
0.4%
33 1
 
0.4%
49 3
 
1.1%
52 1
 
0.4%
53 1
 
0.4%
ValueCountFrequency (%)
81769 1
0.4%
2133 1
0.4%
1978 1
0.4%
1788 1
0.4%
1712 1
0.4%
1580 1
0.4%
1447 1
0.4%
1289 1
0.4%
1142 1
0.4%
1080 1
0.4%

High
Real number (ℝ)

ZEROS 

Distinct64
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.873188
Minimum0
Maximum4384
Zeros149
Zeros (%)54.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-07-02T18:22:54.877414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q319.25
95-th percentile75.5
Maximum4384
Range4384
Interquartile range (IQR)19.25

Descriptive statistics

Standard deviation264.59626
Coefficient of variation (CV)8.5704222
Kurtosis269.24791
Mean30.873188
Median Absolute Deviation (MAD)0
Skewness16.313311
Sum8521
Variance70011.18
MonotonicityNot monotonic
2023-07-02T18:22:54.947883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 149
54.0%
1 10
 
3.6%
18 6
 
2.2%
5 5
 
1.8%
2 5
 
1.8%
32 5
 
1.8%
16 4
 
1.4%
12 4
 
1.4%
13 4
 
1.4%
45 3
 
1.1%
Other values (54) 81
29.3%
ValueCountFrequency (%)
0 149
54.0%
1 10
 
3.6%
2 5
 
1.8%
5 5
 
1.8%
6 3
 
1.1%
7 1
 
0.4%
8 3
 
1.1%
9 2
 
0.7%
10 3
 
1.1%
11 3
 
1.1%
ValueCountFrequency (%)
4384 1
0.4%
252 1
0.4%
151 1
0.4%
141 1
0.4%
119 1
0.4%
111 1
0.4%
103 1
0.4%
95 1
0.4%
91 1
0.4%
90 1
0.4%

Others-%
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct213
Distinct (%)77.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.280708
Minimum0
Maximum84.380952
Zeros64
Zeros (%)23.2%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-07-02T18:22:55.021607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.42940638
median14.709841
Q331.680478
95-th percentile65.760806
Maximum84.380952
Range84.380952
Interquartile range (IQR)31.251072

Descriptive statistics

Standard deviation21.359084
Coefficient of variation (CV)1.0531725
Kurtosis-0.098469549
Mean20.280708
Median Absolute Deviation (MAD)14.709841
Skewness0.94151802
Sum5597.4754
Variance456.21049
MonotonicityNot monotonic
2023-07-02T18:22:55.091833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 64
 
23.2%
22.92051756 1
 
0.4%
4.238921002 1
 
0.4%
2.645788337 1
 
0.4%
23.57343312 1
 
0.4%
72.57963773 1
 
0.4%
5.652759085 1
 
0.4%
29.72440945 1
 
0.4%
70.06903353 1
 
0.4%
25.18555171 1
 
0.4%
Other values (203) 203
73.6%
ValueCountFrequency (%)
0 64
23.2%
0.03620564808 1
 
0.4%
0.08333333333 1
 
0.4%
0.1453065969 1
 
0.4%
0.2029769959 1
 
0.4%
0.396039604 1
 
0.4%
0.4405286344 1
 
0.4%
0.5952380952 1
 
0.4%
0.6684771255 1
 
0.4%
0.6860158311 1
 
0.4%
ValueCountFrequency (%)
84.38095238 1
0.4%
76.80067002 1
0.4%
72.76887872 1
0.4%
72.57963773 1
0.4%
72.0276873 1
0.4%
71.28982129 1
0.4%
70.06903353 1
0.4%
69.59798995 1
0.4%
69.35558646 1
0.4%
69.31092437 1
0.4%

District Name_y
Text

UNIQUE 

Distinct276
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
2023-07-02T18:22:55.233040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length61
Median length56
Mean length16.474638
Min length3

Characters and Unicode

Total characters4547
Distinct characters55
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique276 ?
Unique (%)100.0%

Sample

1st rowAbington
2nd rowAcademy Of the Pacific Rim Charter Public (District)
3rd rowActon-Boxborough
4th rowAdvanced Math and Science Academy Charter (District)
5th rowAgawam
ValueCountFrequency (%)
district 34
 
6.2%
regional 28
 
5.1%
charter 27
 
5.0%
vocational 22
 
4.0%
technical 21
 
3.9%
school 16
 
2.9%
academy 10
 
1.8%
public 7
 
1.3%
north 7
 
1.3%
of 6
 
1.1%
Other values (304) 367
67.3%
2023-07-02T18:22:55.479641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 380
 
8.4%
o 350
 
7.7%
a 327
 
7.2%
t 317
 
7.0%
r 306
 
6.7%
i 289
 
6.4%
n 271
 
6.0%
269
 
5.9%
l 261
 
5.7%
c 189
 
4.2%
Other values (45) 1588
34.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3616
79.5%
Uppercase Letter 576
 
12.7%
Space Separator 269
 
5.9%
Open Punctuation 28
 
0.6%
Close Punctuation 28
 
0.6%
Dash Punctuation 27
 
0.6%
Other Punctuation 3
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 380
10.5%
o 350
9.7%
a 327
9.0%
t 317
8.8%
r 306
8.5%
i 289
 
8.0%
n 271
 
7.5%
l 261
 
7.2%
c 189
 
5.2%
h 172
 
4.8%
Other values (15) 754
20.9%
Uppercase Letter
ValueCountFrequency (%)
S 62
 
10.8%
C 57
 
9.9%
R 45
 
7.8%
D 45
 
7.8%
M 37
 
6.4%
A 36
 
6.2%
B 35
 
6.1%
W 33
 
5.7%
T 33
 
5.7%
V 30
 
5.2%
Other values (13) 163
28.3%
Other Punctuation
ValueCountFrequency (%)
: 1
33.3%
' 1
33.3%
. 1
33.3%
Space Separator
ValueCountFrequency (%)
269
100.0%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4192
92.2%
Common 355
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 380
 
9.1%
o 350
 
8.3%
a 327
 
7.8%
t 317
 
7.6%
r 306
 
7.3%
i 289
 
6.9%
n 271
 
6.5%
l 261
 
6.2%
c 189
 
4.5%
h 172
 
4.1%
Other values (38) 1330
31.7%
Common
ValueCountFrequency (%)
269
75.8%
( 28
 
7.9%
) 28
 
7.9%
- 27
 
7.6%
: 1
 
0.3%
' 1
 
0.3%
. 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4547
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 380
 
8.4%
o 350
 
7.7%
a 327
 
7.2%
t 317
 
7.0%
r 306
 
6.7%
i 289
 
6.4%
n 271
 
6.0%
269
 
5.9%
l 261
 
5.7%
c 189
 
4.2%
Other values (45) 1588
34.9%

Tests Taken
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct128
Distinct (%)46.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125.98551
Minimum0
Maximum18046
Zeros59
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-07-02T18:22:55.574654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median38.5
Q379.25
95-th percentile228.5
Maximum18046
Range18046
Interquartile range (IQR)72.25

Descriptive statistics

Standard deviation1085.5973
Coefficient of variation (CV)8.6168424
Kurtosis272.9117
Mean125.98551
Median Absolute Deviation (MAD)34.5
Skewness16.475508
Sum34772
Variance1178521.4
MonotonicityNot monotonic
2023-07-02T18:22:55.649161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 59
 
21.4%
37 5
 
1.8%
15 4
 
1.4%
24 4
 
1.4%
56 4
 
1.4%
59 4
 
1.4%
7 4
 
1.4%
29 3
 
1.1%
31 3
 
1.1%
5 3
 
1.1%
Other values (118) 183
66.3%
ValueCountFrequency (%)
0 59
21.4%
2 1
 
0.4%
3 2
 
0.7%
4 1
 
0.4%
5 3
 
1.1%
6 1
 
0.4%
7 4
 
1.4%
8 1
 
0.4%
9 3
 
1.1%
10 2
 
0.7%
ValueCountFrequency (%)
18046 1
0.4%
629 1
0.4%
559 1
0.4%
510 1
0.4%
305 1
0.4%
275 1
0.4%
261 1
0.4%
260 1
0.4%
249 2
0.7%
240 2
0.7%

% Score 1-2
Real number (ℝ)

ZEROS 

Distinct181
Distinct (%)65.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.25747155
Minimum0
Maximum0.94117647
Zeros79
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-07-02T18:22:55.731051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.22855179
Q30.42953668
95-th percentile0.690625
Maximum0.94117647
Range0.94117647
Interquartile range (IQR)0.42953668

Descriptive statistics

Standard deviation0.24489572
Coefficient of variation (CV)0.95115642
Kurtosis-0.52430563
Mean0.25747155
Median Absolute Deviation (MAD)0.22855179
Skewness0.65485936
Sum71.062148
Variance0.059973913
MonotonicityNot monotonic
2023-07-02T18:22:55.810086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 79
28.6%
0.5 4
 
1.4%
0.375 4
 
1.4%
0.6666666667 3
 
1.1%
0.3333333333 3
 
1.1%
0.25 2
 
0.7%
0.2916666667 2
 
0.7%
0.6176470588 2
 
0.7%
0.4 2
 
0.7%
0.2413793103 2
 
0.7%
Other values (171) 173
62.7%
ValueCountFrequency (%)
0 79
28.6%
0.02142857143 1
 
0.4%
0.0303030303 1
 
0.4%
0.03137254902 1
 
0.4%
0.03225806452 1
 
0.4%
0.03333333333 1
 
0.4%
0.03636363636 1
 
0.4%
0.03910614525 1
 
0.4%
0.0404040404 1
 
0.4%
0.04545454545 1
 
0.4%
ValueCountFrequency (%)
0.9411764706 1
0.4%
0.9310344828 1
0.4%
0.875 1
0.4%
0.8709677419 1
0.4%
0.8571428571 1
0.4%
0.8245614035 1
0.4%
0.8157894737 1
0.4%
0.8 1
0.4%
0.7692307692 1
0.4%
0.7272727273 1
0.4%

% Score 3-5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct178
Distinct (%)64.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.42231086
Minimum0
Maximum0.97857143
Zeros82
Zeros (%)29.7%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-07-02T18:22:55.887823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.4710084
Q30.6936323
95-th percentile0.92651515
Maximum0.97857143
Range0.97857143
Interquartile range (IQR)0.6936323

Descriptive statistics

Standard deviation0.33216092
Coefficient of variation (CV)0.7865318
Kurtosis-1.3897515
Mean0.42231086
Median Absolute Deviation (MAD)0.29782287
Skewness-0.041269843
Sum116.5578
Variance0.11033088
MonotonicityNot monotonic
2023-07-02T18:22:55.965915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 82
29.7%
0.3333333333 6
 
2.2%
0.6666666667 6
 
2.2%
0.4285714286 2
 
0.7%
0.3636363636 2
 
0.7%
0.3529411765 2
 
0.7%
0.7083333333 2
 
0.7%
0.5 2
 
0.7%
0.6333333333 2
 
0.7%
0.6111111111 2
 
0.7%
Other values (168) 168
60.9%
ValueCountFrequency (%)
0 82
29.7%
0.03448275862 1
 
0.4%
0.06666666667 1
 
0.4%
0.09677419355 1
 
0.4%
0.15 1
 
0.4%
0.1754385965 1
 
0.4%
0.1842105263 1
 
0.4%
0.1875 1
 
0.4%
0.1904761905 1
 
0.4%
0.2051282051 1
 
0.4%
ValueCountFrequency (%)
0.9785714286 1
0.4%
0.9696969697 1
0.4%
0.968627451 1
0.4%
0.9677419355 1
0.4%
0.9636363636 1
0.4%
0.9608938547 1
0.4%
0.9595959596 1
0.4%
0.9466666667 1
0.4%
0.9416666667 1
0.4%
0.9380530973 1
0.4%

Interactions

2023-07-02T18:22:51.919598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:45.787097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:46.455345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:47.204137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:48.023206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:48.693871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:49.393123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:50.010820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:50.635318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:51.302764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:51.986304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:45.867893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:46.527688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:47.272410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:48.096449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:48.778377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:49.467869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:50.077539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:50.707423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:51.368859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:52.049190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:45.934198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:46.590168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:47.372971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:48.161061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:48.852708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:49.526090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:50.142070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:50.777949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:51.429874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:52.107893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:46.000290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:46.652265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:47.578199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:48.224589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:48.927569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:49.589482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:50.206654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:50.845604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:51.490789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:52.172805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:46.065997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:46.717516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:47.645899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:48.293401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:48.997638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:49.654721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:50.271410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:50.912964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:51.563559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:52.245115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:46.136631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:46.794671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:47.715441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:48.369059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:49.073289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:49.720019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:50.340132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:50.983285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:51.628439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:52.304523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:46.200919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:46.922199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:47.775267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:48.433460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:49.137607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:49.775702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:50.397745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:51.048746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:51.685829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:52.360548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:46.261336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:47.000930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:47.836470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:48.498634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:49.200014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:49.831864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:50.452454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:51.108822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:51.744055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:52.427945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:46.328790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:47.072074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:47.902430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:48.567358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:49.266979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:49.895819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:50.514131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:51.176564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:51.807114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:52.491278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:46.390350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:47.138204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:47.962954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:48.630988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:49.331168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:49.951727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:50.574859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:51.239791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-02T18:22:51.862665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-07-02T18:22:56.029834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
District CodeAll GradesTotal StudentsPrimarySecondaryHighOthers-%Tests Taken% Score 1-2% Score 3-5
District Code1.000-0.354-0.433-0.148-0.361-0.122-0.254-0.285-0.151-0.266
All Grades-0.3541.0000.5490.6060.9400.3830.8790.3700.1670.321
Total Students-0.4330.5491.0000.1020.5500.4180.1860.7230.3250.569
Primary-0.1480.6060.1021.0000.4260.0240.708-0.0220.089-0.045
Secondary-0.3610.9400.5500.4261.0000.2900.7800.3730.1610.327
High-0.1220.3830.4180.0240.2901.0000.2470.3470.1050.241
Others-%-0.2540.8790.1860.7080.7800.2471.0000.1030.0840.118
Tests Taken-0.2850.3700.723-0.0220.3730.3470.1031.0000.4480.842
% Score 1-2-0.1510.1670.3250.0890.1610.1050.0840.4481.0000.252
% Score 3-5-0.2660.3210.569-0.0450.3270.2410.1180.8420.2521.000

Missing values

2023-07-02T18:22:52.582777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-02T18:22:52.689475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

District Name_xDistrict CodeAll GradesTotal StudentsPrimarySecondaryHighOthers-%District Name_yTests Taken% Score 1-2% Score 3-5
0Abington10000259.01658.00.0259.00.015.621230Abington25.00.1600000.680000
1Academy Of the Pacific Rim Charter Public (District)41200000.036.00.00.00.00.000000Academy Of the Pacific Rim Charter Public (District)0.00.0000000.000000
2Acton-Boxborough6000000722.04322.00.0686.036.016.705229Acton-Boxborough305.00.0622950.937705
3Advanced Math and Science Academy Charter (District)4300000315.0808.00.0225.090.038.985149Advanced Math and Science Academy Charter (District)135.00.1111110.888889
4Agawam50000883.02789.00.0867.016.031.660093Agawam37.00.4324320.459459
5Amesbury70000773.01481.0310.0407.056.052.194463Amesbury0.00.0000000.000000
6Amherst-Pelham605000034.0849.00.00.034.04.004711Amherst-Pelham73.00.1232880.876712
7Andover900001203.04549.00.01142.061.026.445373Andover140.00.0214290.978571
8Arlington100000317.04818.00.0317.00.06.579494Arlington240.00.1583330.841667
9Ashburnham-Westminster6100000505.01981.00.0493.012.025.492176Ashburnham-Westminster20.00.7000000.250000
District Name_xDistrict CodeAll GradesTotal StudentsPrimarySecondaryHighOthers-%District Name_yTests Taken% Score 1-2% Score 3-5
266Weymouth336000034.04058.00.00.034.00.837851Weymouth92.00.5326090.467391
267Whitman-Hanson78000006.02956.00.00.06.00.202977Whitman-Hanson38.00.5000000.447368
268Whittier Regional Vocational Technical88500000.0847.00.00.00.00.000000Whittier Regional Vocational Technical10.00.0000000.000000
269Wilmington34200001769.02456.0787.0950.032.072.027687Wilmington85.00.5294120.470588
270Winchendon3430000318.0970.0246.072.00.032.783505Winchendon0.00.0000000.000000
271Winchester344000071.03807.00.00.071.01.864986Winchester249.00.0923690.907631
272Winthrop3460000688.01487.0468.0220.00.046.267653Winthrop63.00.6825400.301587
273Woburn3470000614.03113.00.0592.022.019.723739Woburn45.00.5111110.488889
274Worcester3480000322.07797.00.0284.038.04.129794Worcester275.00.5527270.447273
275State Totals0121772.0557504.035619.081769.04384.021.842354State Totals18046.00.3023940.697606